Triple

T9751634
Position Surface form Disambiguated ID Type / Status
Subject Deutschland 86 E236454 entity
Predicate characterFocus P31 FINISHED
Object Martin Rauch E823324 NE FINISHED

How this triple was built (2 steps)

Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.

NER Named-entity recognition gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Martin Rauch | Statement: [Deutschland 86, characterFocus, Martin Rauch]
NED1 Entity disambiguation (via context triple) gpt-5-mini-2025-08-07
Target entity: Martin Rauch
Context triple: [Deutschland 86, characterFocus, Martin Rauch]
  • A. Martin Rauch chosen
    Martin Rauch is the young East German border guard turned undercover spy who serves as the central protagonist in the Cold War television drama "Deutschland 83."
  • B. Carl Hasenauer
    Carl Hasenauer was a prominent 19th-century Austrian architect known for his monumental historicist buildings in Vienna.
  • C. Wolfgang Schmieder
    Wolfgang Schmieder was a German musicologist best known for creating the Bach-Werke-Verzeichnis, the standard thematic catalog of Johann Sebastian Bach’s works.
  • D. Wolfgang Sauer
    Wolfgang Sauer is a German singer known for his popular Schlager and easy-listening recordings in the mid-20th century.
  • E. Albert Schickedanz
    Albert Schickedanz was a Hungarian architect and designer best known for his monumental historicist works in Budapest, including key buildings and ensembles on Andrássy Avenue.
  • F. None of above.
  • G. Unsure - the case is ambiguous/there is not enough information to decide.

Provenance (3 batches)

The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.

Step Stage Batch ID Status When
creating Elicitation batch_69ca84d4eddc8190996fec1417d2bae8 completed March 30, 2026, 2:12 p.m.
NER Named-entity recognition batch_69cd9facd5b881909f0569b23f308815 completed April 1, 2026, 10:43 p.m.
NED1 Entity disambiguation (via context triple) batch_69d1d5a1a3b88190a1b4561a9a780e41 completed April 5, 2026, 3:23 a.m.
Created at: March 30, 2026, 8:24 p.m.